SaoMNK Tutorial: Generalizing NK Epistatic Interaction Fitness
S. Downing
2025-02-11
This project begins from the simple premise that the task of
endogenizing the NK search landscape can be done by representing the
search environment as a binary bipartite network of M
actors affiliating with N components. This bipartite
network can be then be analyzed according to the Stochastic
Actor-Oriented Model (SAOM) (Snijders,
1996).
Thus, the Stochastic Actor-Oriented MNK model, abbrivated
SaoMNK, is designed for running strategic search
simulation, testing, and experimentation by leveraging the
RSiena package, an R implementation of
Simulation Investigation for Empirical Network Analysis (SIENA).
This tutorial offers a basic introduction to SaoMNK.
############### Load R6 Class DEPENDENCIES ############################
## Biparite Environment Search Simulation Class
SaomNkRSienaBiEnv <- source(file.path(dir_r, 'SAOM_NK_R6_model.R'))$value
# ## RSiena search Class
# SaomNkRSienaBiEnv_search_rsiena <- source(file.path(dir_proj, 'SAOM_NK_R6_search_rsiena_model.R'))$value
## default settings: Users do not change; TODO: implment within restricted class attributes
DV_NAME <- 'self$bipartite_rsienaDV'
#
steps_per_actor <- 251. NO Endogenous Noise in Environment (Structural Effects = 0)
1.1 Environment Configuration
#
environ_params <- list(
M = 12, ## Actors
N = 9, ## Components
BI_PROB = 0, ## Environmental Density (DGP hyperparameter)
component_matrix_start = 'rand', ##**TODO** Implement: 'rand','modular','semi-modular',...
rand_seed = 1234,
plot_init = F,
name = '_test_tutorial_nb_'
)
#
env1 <- SaomNkRSienaBiEnv$new(environ_params)##
## TEST FROM CALLED CLASS INIT *BEFORE* BASE INIT
##
## CALLED _BASE_ INIT
##
## TEST FROM CALLED CLASS INIT *AFTER* BASE INITprint(c(env1$M, env1$N))## [1] 12 9library(Matrix) # For block diagonal matrices
#
create_block_diag <- function(N, B) {
# Determine the approximate block size
block_sizes <- rep(N %/% B, B) # Equal-sized blocks
block_sizes[1:(N %% B)] <- if (N %% B == 0) {
block_sizes[1:(N %% B)] # Adjust for remainder
} else {
block_sizes[1:(N %% B)] + 1
}
# Create individual binary blocks
blocks <- lapply(block_sizes, function(s) matrix(1, nrow = s, ncol = s))
# Combine blocks into a block diagonal matrix
block_diag_matrix <- as.matrix(bdiag(blocks)) # Convert sparse to standard matrix
# Ensure exact NxN dimensions
block_diag_matrix <- block_diag_matrix[1:N, 1:N] # Trim to N x N
# return
return(block_diag_matrix)
}
# Example usage:
N <- 6 # Number of rows/columns
B <- 2 # Number of blocks
block_matrix <- create_block_diag(8, 2)
# Print the matrix
print(block_matrix)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 1 1 1 0 0 0 0
## [2,] 1 1 1 1 0 0 0 0
## [3,] 1 1 1 1 0 0 0 0
## [4,] 1 1 1 1 0 0 0 0
## [5,] 0 0 0 0 1 1 1 1
## [6,] 0 0 0 0 1 1 1 1
## [7,] 0 0 0 0 1 1 1 1
## [8,] 0 0 0 0 1 1 1 11.2 Structrual Model
SAOM Objective Function serves as the stochastic actor’s utility function for strategic search.
#
strategies <- list(
egoX = c(-1, 0, 1),
inPopX = c( 1, 0, -1)
)
## 2.b. Component Payoffs vector
set.seed(12345)
component_payoffs <- runif(environ_params$N, min = 0, max = 1)
## 2. Strategies sets the objective function as a linear combination of network stats across DVs
#
actor_strats_list <- lapply(strategies, function(strat) rep(strat, environ_params$M/length(strat)) )
component_int_mat <- create_block_diag(environ_params$N, round(environ_params$N/3))
# dyad_cov_XWX <- ( outer(component_payoffs, component_payoffs, '*') *
# create_block_diag(environ_params$N, round(environ_params$N/3))
# )
dyad_cov_X <- matrix(runif(n = environ_params$N*environ_params$M, min = -.5, max= 1), nrow=environ_params$M)
# dyad_cov_X <- matrix(runif(environ_params$N*environ_params$M) - runif(environ_params$N*environ_params$M),
# nrow=environ_params$M)
# dyad_cov_XWX_X <- t( dyad_cov_XWX %*% t(dyad_cov_X) )
#
structure_model <- list(
dv_bipartite = list(
name = 'self$bipartite_rsienaDV',
effects = list( ##**STRUCTURAL EFFECTS -- dyadic/network endogeneity sources**
list(effect='density', parameter= 0, dv_name=DV_NAME, fix=T ), ##interaction1 = NULL
list(effect='inPop', parameter= 0, dv_name=DV_NAME, fix=T ), #interaction1 = NUL
list(effect='outAct', parameter= 0, dv_name=DV_NAME, fix=T )
),
## COVARIATE EFFECTS
coCovars = list(
##** COMPONENTS : MONADIC CONSTANT COVARIATE EFFECTS **##
# list(effect='altX', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$component_1_coCovar', x = component_payoffs
# ),
# list(effect='outActX', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$component_1_coCovar', x = component_payoffs
# ),
##** STRATEGIES : MONADIC CONSTANT COVARIATE EFFECTS **##
list(effect='egoX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_1_coCovar', x = actor_strats_list[[1]]
), #interaction1 = NULL
list(effect='inPopX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_2_coCovar', x = actor_strats_list[[2]]
) #,
# list(effect='totInDist2', parameter= 0, dv_name=DV_NAME, fix=T,
# interaction1='self$strat_3_coCovar', x = (actor_strats_list[[1]] - actor_strats_list[[2]] )
# )
),
##**MONADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC STRATEGY PROGRAMS**
varCovars = list(),
##**DYADIC CONSTANT COVARIATE EFFECTS -- EXOGENOUS INTERACTION MATRIX**
coDyadCovars = list(
list(effect='XWX', parameter= 1, dv_name=DV_NAME, fix=T,
interaction1='self$component_1_coDyadCovar',
x = component_int_mat ## component-[actor]-component dyads
) ,
list(effect='X', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$component_2_coDyadCovar',
x = dyad_cov_X ## deltas = changes of payoff contributions from each actor-component
)
),
##**DYADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC INTERACTION MATRIX**
varDyadCovars = list(),
interactions = list(
list(effect='egoX|XWX', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_1_coCovar',
interaction2='self$component_1_coDyadCovar'
),
list(effect='inPopX|X', parameter= 0, dv_name=DV_NAME, fix=T,
interaction1='self$strat_2_coCovar',
interaction2='self$component_2_coDyadCovar'
)
)
)
)env1$preview_effects(structure_model, filter=FALSE)## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
## Effects documentation written to file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_rsiena_effects_doc_.html .# ## Uncomment for HTML output with filterable data table
env1$preview_effects(structure_model, filter=TRUE)## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
## Effects documentation written to file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_rsiena_effects_doc_.html .1.3 Run RSiena Search Process
## TODO: PICK UP WITH coDydCovar Interation Matrix
## Run Rsiena search using variable parameters in theta_matrix
env1$search_rsiena(
structure_model,
iterations = env1$M * steps_per_actor,
digits = 4,
run_seed = 12345
)## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
##
##
## self$rsiena_data :
##
## Dependent variables: self$bipartite_rsienaDV
## Number of observations: 2
##
## Nodesets ACTORS COMPONENTS
## Number of nodes 12 9
##
## Dependent variable self$bipartite_rsienaDV
## Type bipartite
## Observations 2
## First nodeset ACTORS
## Second nodeset COMPONENTS
## Densities NA NA
##
## Constant covariates: self$strat_1_coCovar, self$strat_2_coCovar
## Constant dyadic covariates: self$component_1_coDyadCovar, self$component_2_coDyadCovar
##
## structural effects i=1, j=1
## $effect
## [1] "density"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE -1.60944 0
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=2
## $effect
## [1] "inPop"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=3
## $effect
## [1] "outAct"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
##
## coCovars i=1, j=1
## $effect
## [1] "egoX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $x
## [1] -1 0 1 -1 0 1 -1 0 1 -1 0 1
##
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
##
## coCovars i=1, j=2
## $effect
## [1] "inPopX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $x
## [1] 1 0 -1 1 0 -1 1 0 -1 1 0 -1
##
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 1
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 0
##
## coDyadCovars i=1, j=1
## $effect
## [1] "XWX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 1 0 0 0 0 0 0
## [2,] 1 1 1 0 0 0 0 0 0
## [3,] 1 1 1 0 0 0 0 0 0
## [4,] 0 0 0 1 1 1 0 0 0
## [5,] 0 0 0 1 1 1 0 0 0
## [6,] 0 0 0 1 1 1 0 0 0
## [7,] 0 0 0 0 0 0 1 1 1
## [8,] 0 0 0 0 0 0 1 1 1
## [9,] 0 0 0 0 0 0 1 1 1
##
## effectName include fix test initialValue
## 1 XW=>X closure of self$component_1_coDyadCovar TRUE TRUE FALSE 0
## parm
## 1 0
## effectName include fix test initialValue
## 1 XW=>X closure of self$component_1_coDyadCovar TRUE TRUE FALSE 0
## parm
## 1 1
##
## coDyadCovars i=1, j=2
## $effect
## [1] "X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_2_coDyadCovar"
##
## $x
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.98460541 -0.009871387 0.52275043 -0.01816299 -0.3794959 0.919646128
## [2,] -0.44819685 0.948122985 0.05515619 -0.40970726 0.1532957 0.532530035
## [3,] -0.27143976 0.561222816 0.04243836 -0.43481532 -0.1451293 0.258300585
## [4,] 0.60352743 0.466813955 0.80319235 -0.41741927 0.6873517 0.059404367
## [5,] -0.49829512 0.084742727 0.85623200 0.43831420 -0.1119735 0.003707558
## [6,] 0.08680500 0.547815459 0.42613685 0.94670543 0.9789757 -0.427622971
## [7,] 0.19374198 0.316086797 -0.29895255 0.74095430 0.6353106 0.428421309
## [8,] 0.08221597 -0.160299232 0.67328992 -0.02745765 0.9696674 0.942170938
## [9,] 0.10372771 0.226836633 0.14379823 -0.18046182 -0.1715782 0.482440764
## [10,] -0.23155462 0.689510755 0.89091096 0.59874418 0.9230608 0.265437987
## [11,] 0.92748813 -0.491018556 0.65986484 0.24886153 -0.2758131 -0.274852684
## [12,] 0.18059211 -0.218431331 -0.11047813 0.59465796 0.4005355 0.805671821
## [,7] [,8] [,9]
## [1,] 0.27166252 0.4013924 0.85375569
## [2,] -0.48702813 0.5721698 0.45618165
## [3,] -0.47120785 0.2707685 0.79645170
## [4,] -0.28323215 0.5801748 -0.12332339
## [5,] -0.04245237 0.6249194 -0.17739638
## [6,] 0.73848529 -0.3565392 0.41421407
## [7,] 0.25351696 0.0967388 0.07516695
## [8,] 0.70535894 -0.0583019 0.63290657
## [9,] -0.40904003 0.4258805 0.06960436
## [10,] 0.89193270 0.9614112 0.69245810
## [11,] 0.71226783 0.4273181 0.85853670
## [12,] -0.38177999 0.2820538 0.97603926
##
## There is no effect with short name X,
## and with interaction1 = <>, interaction2 = <>, and type = <eval>,
## for dependent variable self$bipartite_rsienaDV .
## See effectsDocumentation() for this effects object.
## effectName include fix test initialValue parm
## 1 self$component_2_coDyadCovar TRUE TRUE FALSE 0 0
##
## interactions i=1, j=1
## $effect
## [1] "egoX|XWX"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $interaction2
## [1] "self$component_1_coDyadCovar"
##
## $effects
## [1] "egoX" "XWX"
##
## effectName
## 1 self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar
## include fix test initialValue parm effect1 effect2
## 1 TRUE TRUE FALSE 0 0 88 80
##
## interactions i=1, j=2
## $effect
## [1] "inPopX|X"
##
## $parameter
## [1] 0
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $interaction2
## [1] "self$component_2_coDyadCovar"
##
## $effects
## [1] "inPopX" "X"
##
## effectName
## 1 ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar
## include fix test initialValue parm effect1 effect2
## 1 TRUE TRUE FALSE 0 0 169 85
## [1] "density"
## [1] "inPop"
## [1] "outAct"
## [1] "XWX"
## [1] "X"
## [1] "egoX"
## [1] "inPopX"
## [1] "egoX|XWX"
## [1] "inPopX|X"
##
##
## theta_matrix :
##
## density inPop outAct XWX X egoX inPopX egoX|XWX inPopX|X
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## If you use this algorithm object, siena07 will create/use an output file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_test_tutorial_nb__173926268866.txt .
##
## Start phase 0
## theta: 0 0 0 0 0 0 0 0 0
##
## Start phase 3
## Phase 3 Iteration 100 Progress 33%
## Phase 3 Iteration 200 Progress 67%
## Phase 3 Iteration 300 Progress 100%
## Parameter values used for simulations
##
## Mean Standard
## value Deviation
##
## Rate parameters:
## 0 Rate parameter NA ( NA )
##
## Other parameters:
## 1. eval outdegree (density) 0 ( NA )
## 2. eval indegree - popularity 0 ( NA )
## 3. eval outdegree - activity 0 ( NA )
## 4. eval XW=>X closure of self$component_1_coDyadCovar 1 ( NA )
## 5. eval self$component_2_coDyadCovar 0 ( NA )
## 6. eval self$strat_1_coCovar ego 0 ( NA )
## 7. eval ind. pop.^(1/0) weighted self$strat_2_coCovar 0 ( NA )
## 8. eval self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0 ( NA )
## 9. eval ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0 ( NA )
##
## Simulated means and standard deviations
## 1. Number of ties 0.867 0.341
## 2. Sum of squared indegrees 0.867 0.341
## 3. Sum of squared outdegrees 0.867 0.341
## 4. XW=>X closure of self$component_1_coDyadCovar 0.000 0.000
## 5. Sum of ties x self$component_2_coDyadCovar -0.037 0.418
## 6. Sum of outdegrees x self$strat_1_coCovar -0.063 0.758
## 7. indegree pop.^(1/0) weighted self$strat_2_coCovar 0.063 0.758
## 8. self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0.000 0.000
## 9. ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0.071 0.316
##
##
## Simulated statistics are in x$sf
## and simulated dependent variables in x$sims, where x is the created object.
##
## Total of 300 iteration steps.
##
## Covariance matrix of estimates (correlations below diagonal)
##
## 0 0 0 0 0 0 0 0 0
## NaN 0 0 0 0 0 0 0 0
## NaN NaN 0 0 0 0 0 0 0
## NaN NaN NaN 0 0 0 0 0 0
## NaN NaN NaN NaN 0 0 0 0 0
## NaN NaN NaN NaN NaN 0 0 0 0
## NaN NaN NaN NaN NaN NaN 0 0 0
## NaN NaN NaN NaN NaN NaN NaN 0 0
## NaN NaN NaN NaN NaN NaN NaN NaN 0
##
## Derivative matrix of expected statistics X by parameters:
##
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## -0.005 -0.005 -0.005 0.000 0.152 -0.018 0.018 0.000 0.008
## 0.007 0.007 0.007 0.000 -0.008 0.057 -0.057 0.000 0.003
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
##
## Covariance matrix of X (correlations below diagonal):
##
## 0.116 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 1.000 0.116 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## 1.000 1.000 0.116 0.000 -0.005 -0.008 0.008 0.000 0.010
## NaN NaN NaN 0.000 0.000 0.000 0.000 0.000 0.000
## -0.035 -0.035 -0.035 NaN 0.175 -0.074 0.074 0.000 0.008
## -0.033 -0.033 -0.033 NaN -0.233 0.575 -0.575 0.000 0.029
## 0.033 0.033 0.033 NaN 0.233 -1.000 0.575 0.000 -0.029
## NaN NaN NaN NaN NaN NaN NaN 0.000 0.000
## 0.089 0.089 0.089 NaN 0.059 0.119 -0.119 NaN 0.100
##
##
##
## Simulated Decision Chain Summary:
##
## dv_type dv_type_bin dv_varname id_from
## Length:300 Min. :0 Length:300 Min. : 1.000
## Class :character 1st Qu.:0 Class :character 1st Qu.: 4.000
## Mode :character Median :0 Mode :character Median : 7.000
## Mean :0 Mean : 6.757
## 3rd Qu.:0 3rd Qu.:10.000
## Max. :0 Max. :12.000
## id_to beh_difference reciprocal_rate LogOptionSetProb
## Min. : 1.0 Min. :0 Min. :0.08333 Min. :-2.485
## 1st Qu.: 3.0 1st Qu.:0 1st Qu.:0.08333 1st Qu.:-2.485
## Median : 6.0 Median :0 Median :0.08333 Median :-2.485
## Mean : 5.6 Mean :0 Mean :0.08333 Mean :-2.485
## 3rd Qu.: 8.0 3rd Qu.:0 3rd Qu.:0.08333 3rd Qu.:-2.485
## Max. :10.0 Max. :0 Max. :0.08333 Max. :-2.485
## LogChoiceProb diagonal stability tie_change
## Min. :-2.303 Length:300 Mode :logical Mode :logical
## 1st Qu.:-2.303 Class :character FALSE:260 FALSE:40
## Median :-2.303 Mode :character TRUE :40 TRUE :260
## Mean :-2.303
## 3rd Qu.:-2.303
## Max. :-2.303
## chain_step_id
## Min. : 1.00
## 1st Qu.: 75.75
## Median :150.50
## Mean :150.34
## 3rd Qu.:225.25
## Max. :300.00
## [1] 300 13
## dv_type dv_type_bin dv_varname id_from id_to beh_difference
## 1 Network 0 self$bipartite_rsienaDV 11 8 0
## 2 Network 0 self$bipartite_rsienaDV 4 6 0
## 3 Network 0 self$bipartite_rsienaDV 12 1 0
## 4 Network 0 self$bipartite_rsienaDV 9 1 0
## 5 Network 0 self$bipartite_rsienaDV 6 4 0
## 6 Network 0 self$bipartite_rsienaDV 3 10 0
## 7 Network 0 self$bipartite_rsienaDV 9 7 0
## 8 Network 0 self$bipartite_rsienaDV 9 6 0
## 9 Network 0 self$bipartite_rsienaDV 1 2 0
## 10 Network 0 self$bipartite_rsienaDV 5 4 0
## 11 Network 0 self$bipartite_rsienaDV 2 8 0
## 12 Network 0 self$bipartite_rsienaDV 4 4 0
## 13 Network 0 self$bipartite_rsienaDV 8 10 0
## 14 Network 0 self$bipartite_rsienaDV 4 3 0
## 15 Network 0 self$bipartite_rsienaDV 6 8 0
## 16 Network 0 self$bipartite_rsienaDV 6 3 0
## 17 Network 0 self$bipartite_rsienaDV 4 10 0
## 18 Network 0 self$bipartite_rsienaDV 12 3 0
## 19 Network 0 self$bipartite_rsienaDV 12 7 0
## 20 Network 0 self$bipartite_rsienaDV 5 4 0
## 21 Network 0 self$bipartite_rsienaDV 8 10 0
## 22 Network 0 self$bipartite_rsienaDV 7 2 0
## 23 Network 0 self$bipartite_rsienaDV 1 2 0
## 24 Network 0 self$bipartite_rsienaDV 10 6 0
## 25 Network 0 self$bipartite_rsienaDV 1 10 0
## 26 Network 0 self$bipartite_rsienaDV 1 7 0
## 27 Network 0 self$bipartite_rsienaDV 7 8 0
## 28 Network 0 self$bipartite_rsienaDV 2 4 0
## 29 Network 0 self$bipartite_rsienaDV 8 10 0
## 30 Network 0 self$bipartite_rsienaDV 7 10 0
## 31 Network 0 self$bipartite_rsienaDV 11 3 0
## 32 Network 0 self$bipartite_rsienaDV 10 4 0
## 33 Network 0 self$bipartite_rsienaDV 11 10 0
## 34 Network 0 self$bipartite_rsienaDV 1 4 0
## 35 Network 0 self$bipartite_rsienaDV 7 1 0
## 36 Network 0 self$bipartite_rsienaDV 9 9 0
## 37 Network 0 self$bipartite_rsienaDV 10 1 0
## 38 Network 0 self$bipartite_rsienaDV 10 7 0
## 39 Network 0 self$bipartite_rsienaDV 3 1 0
## 40 Network 0 self$bipartite_rsienaDV 7 7 0
## 41 Network 0 self$bipartite_rsienaDV 12 10 0
## 42 Network 0 self$bipartite_rsienaDV 7 8 0
## 43 Network 0 self$bipartite_rsienaDV 3 8 0
## 44 Network 0 self$bipartite_rsienaDV 6 9 0
## 45 Network 0 self$bipartite_rsienaDV 9 6 0
## 46 Network 0 self$bipartite_rsienaDV 6 10 0
## 47 Network 0 self$bipartite_rsienaDV 11 4 0
## 48 Network 0 self$bipartite_rsienaDV 5 8 0
## 49 Network 0 self$bipartite_rsienaDV 12 8 0
## 50 Network 0 self$bipartite_rsienaDV 3 5 0
## 51 Network 0 self$bipartite_rsienaDV 4 10 0
## 52 Network 0 self$bipartite_rsienaDV 5 10 0
## 53 Network 0 self$bipartite_rsienaDV 2 8 0
## 54 Network 0 self$bipartite_rsienaDV 12 1 0
## 55 Network 0 self$bipartite_rsienaDV 10 3 0
## 56 Network 0 self$bipartite_rsienaDV 2 6 0
## 57 Network 0 self$bipartite_rsienaDV 10 1 0
## 58 Network 0 self$bipartite_rsienaDV 7 7 0
## 59 Network 0 self$bipartite_rsienaDV 9 4 0
## 60 Network 0 self$bipartite_rsienaDV 12 5 0
## 61 Network 0 self$bipartite_rsienaDV 7 7 0
## 62 Network 0 self$bipartite_rsienaDV 2 2 0
## 63 Network 0 self$bipartite_rsienaDV 7 9 0
## 64 Network 0 self$bipartite_rsienaDV 11 5 0
## 65 Network 0 self$bipartite_rsienaDV 7 5 0
## 66 Network 0 self$bipartite_rsienaDV 9 6 0
## 67 Network 0 self$bipartite_rsienaDV 12 10 0
## 68 Network 0 self$bipartite_rsienaDV 9 10 0
## 69 Network 0 self$bipartite_rsienaDV 5 8 0
## 70 Network 0 self$bipartite_rsienaDV 11 9 0
## 71 Network 0 self$bipartite_rsienaDV 3 2 0
## 72 Network 0 self$bipartite_rsienaDV 10 10 0
## 73 Network 0 self$bipartite_rsienaDV 1 2 0
## 74 Network 0 self$bipartite_rsienaDV 12 7 0
## 75 Network 0 self$bipartite_rsienaDV 4 2 0
## 76 Network 0 self$bipartite_rsienaDV 5 9 0
## 77 Network 0 self$bipartite_rsienaDV 10 1 0
## 78 Network 0 self$bipartite_rsienaDV 4 5 0
## 79 Network 0 self$bipartite_rsienaDV 10 6 0
## 80 Network 0 self$bipartite_rsienaDV 9 9 0
## 81 Network 0 self$bipartite_rsienaDV 10 3 0
## 82 Network 0 self$bipartite_rsienaDV 1 1 0
## 83 Network 0 self$bipartite_rsienaDV 9 7 0
## 84 Network 0 self$bipartite_rsienaDV 9 3 0
## 85 Network 0 self$bipartite_rsienaDV 5 5 0
## 86 Network 0 self$bipartite_rsienaDV 11 6 0
## 87 Network 0 self$bipartite_rsienaDV 4 9 0
## 88 Network 0 self$bipartite_rsienaDV 9 7 0
## 89 Network 0 self$bipartite_rsienaDV 5 10 0
## 90 Network 0 self$bipartite_rsienaDV 1 2 0
## 91 Network 0 self$bipartite_rsienaDV 9 7 0
## 92 Network 0 self$bipartite_rsienaDV 9 5 0
## 93 Network 0 self$bipartite_rsienaDV 11 9 0
## 94 Network 0 self$bipartite_rsienaDV 8 8 0
## 95 Network 0 self$bipartite_rsienaDV 9 7 0
## 96 Network 0 self$bipartite_rsienaDV 8 6 0
## 97 Network 0 self$bipartite_rsienaDV 10 2 0
## 98 Network 0 self$bipartite_rsienaDV 6 3 0
## 99 Network 0 self$bipartite_rsienaDV 9 9 0
## 100 Network 0 self$bipartite_rsienaDV 10 6 0
## 101 Network 0 self$bipartite_rsienaDV 10 10 0
## 102 Network 0 self$bipartite_rsienaDV 8 5 0
## 103 Network 0 self$bipartite_rsienaDV 6 8 0
## 104 Network 0 self$bipartite_rsienaDV 4 7 0
## 105 Network 0 self$bipartite_rsienaDV 11 7 0
## 106 Network 0 self$bipartite_rsienaDV 11 1 0
## 107 Network 0 self$bipartite_rsienaDV 11 9 0
## 108 Network 0 self$bipartite_rsienaDV 4 2 0
## 109 Network 0 self$bipartite_rsienaDV 5 6 0
## 110 Network 0 self$bipartite_rsienaDV 1 5 0
## 111 Network 0 self$bipartite_rsienaDV 3 6 0
## 112 Network 0 self$bipartite_rsienaDV 11 3 0
## 113 Network 0 self$bipartite_rsienaDV 1 6 0
## 114 Network 0 self$bipartite_rsienaDV 1 1 0
## 115 Network 0 self$bipartite_rsienaDV 10 6 0
## 116 Network 0 self$bipartite_rsienaDV 6 2 0
## 117 Network 0 self$bipartite_rsienaDV 4 2 0
## 118 Network 0 self$bipartite_rsienaDV 5 9 0
## 119 Network 0 self$bipartite_rsienaDV 8 1 0
## 120 Network 0 self$bipartite_rsienaDV 4 1 0
## 121 Network 0 self$bipartite_rsienaDV 1 9 0
## 122 Network 0 self$bipartite_rsienaDV 2 9 0
## 123 Network 0 self$bipartite_rsienaDV 1 7 0
## 124 Network 0 self$bipartite_rsienaDV 1 10 0
## 125 Network 0 self$bipartite_rsienaDV 10 6 0
## 126 Network 0 self$bipartite_rsienaDV 12 3 0
## 127 Network 0 self$bipartite_rsienaDV 2 6 0
## 128 Network 0 self$bipartite_rsienaDV 7 4 0
## 129 Network 0 self$bipartite_rsienaDV 5 9 0
## 130 Network 0 self$bipartite_rsienaDV 4 5 0
## 131 Network 0 self$bipartite_rsienaDV 8 3 0
## 132 Network 0 self$bipartite_rsienaDV 9 8 0
## 133 Network 0 self$bipartite_rsienaDV 2 3 0
## 134 Network 0 self$bipartite_rsienaDV 4 6 0
## 135 Network 0 self$bipartite_rsienaDV 8 2 0
## 136 Network 0 self$bipartite_rsienaDV 10 9 0
## 137 Network 0 self$bipartite_rsienaDV 10 8 0
## 138 Network 0 self$bipartite_rsienaDV 11 10 0
## 139 Network 0 self$bipartite_rsienaDV 11 1 0
## 140 Network 0 self$bipartite_rsienaDV 5 8 0
## 141 Network 0 self$bipartite_rsienaDV 5 4 0
## 142 Network 0 self$bipartite_rsienaDV 4 7 0
## 143 Network 0 self$bipartite_rsienaDV 10 8 0
## 144 Network 0 self$bipartite_rsienaDV 11 6 0
## 145 Network 0 self$bipartite_rsienaDV 7 10 0
## 146 Network 0 self$bipartite_rsienaDV 6 4 0
## 147 Network 0 self$bipartite_rsienaDV 8 7 0
## 148 Network 0 self$bipartite_rsienaDV 2 10 0
## 149 Network 0 self$bipartite_rsienaDV 5 3 0
## 150 Network 0 self$bipartite_rsienaDV 3 6 0
## 151 Network 0 self$bipartite_rsienaDV 7 1 0
## 152 Network 0 self$bipartite_rsienaDV 8 8 0
## 153 Network 0 self$bipartite_rsienaDV 11 4 0
## 154 Network 0 self$bipartite_rsienaDV 3 1 0
## 155 Network 0 self$bipartite_rsienaDV 5 1 0
## 156 Network 0 self$bipartite_rsienaDV 7 7 0
## 157 Network 0 self$bipartite_rsienaDV 4 10 0
## 158 Network 0 self$bipartite_rsienaDV 9 3 0
## 159 Network 0 self$bipartite_rsienaDV 8 6 0
## 160 Network 0 self$bipartite_rsienaDV 11 3 0
## 161 Network 0 self$bipartite_rsienaDV 7 4 0
## 162 Network 0 self$bipartite_rsienaDV 3 4 0
## 163 Network 0 self$bipartite_rsienaDV 9 2 0
## 164 Network 0 self$bipartite_rsienaDV 7 2 0
## 165 Network 0 self$bipartite_rsienaDV 10 3 0
## 166 Network 0 self$bipartite_rsienaDV 3 10 0
## 167 Network 0 self$bipartite_rsienaDV 10 7 0
## 168 Network 0 self$bipartite_rsienaDV 7 8 0
## 169 Network 0 self$bipartite_rsienaDV 10 9 0
## 170 Network 0 self$bipartite_rsienaDV 7 2 0
## 171 Network 0 self$bipartite_rsienaDV 1 4 0
## 172 Network 0 self$bipartite_rsienaDV 12 3 0
## 173 Network 0 self$bipartite_rsienaDV 11 5 0
## 174 Network 0 self$bipartite_rsienaDV 12 6 0
## 175 Network 0 self$bipartite_rsienaDV 4 1 0
## 176 Network 0 self$bipartite_rsienaDV 6 10 0
## 177 Network 0 self$bipartite_rsienaDV 8 6 0
## 178 Network 0 self$bipartite_rsienaDV 4 9 0
## 179 Network 0 self$bipartite_rsienaDV 9 10 0
## 180 Network 0 self$bipartite_rsienaDV 4 7 0
## 181 Network 0 self$bipartite_rsienaDV 8 10 0
## 182 Network 0 self$bipartite_rsienaDV 2 10 0
## 183 Network 0 self$bipartite_rsienaDV 9 10 0
## 184 Network 0 self$bipartite_rsienaDV 6 9 0
## 185 Network 0 self$bipartite_rsienaDV 8 6 0
## 186 Network 0 self$bipartite_rsienaDV 1 1 0
## 187 Network 0 self$bipartite_rsienaDV 4 7 0
## 188 Network 0 self$bipartite_rsienaDV 12 2 0
## 189 Network 0 self$bipartite_rsienaDV 11 9 0
## 190 Network 0 self$bipartite_rsienaDV 9 9 0
## 191 Network 0 self$bipartite_rsienaDV 11 2 0
## 192 Network 0 self$bipartite_rsienaDV 1 8 0
## 193 Network 0 self$bipartite_rsienaDV 6 7 0
## 194 Network 0 self$bipartite_rsienaDV 6 3 0
## 195 Network 0 self$bipartite_rsienaDV 9 2 0
## 196 Network 0 self$bipartite_rsienaDV 11 4 0
## 197 Network 0 self$bipartite_rsienaDV 12 7 0
## 198 Network 0 self$bipartite_rsienaDV 7 5 0
## 199 Network 0 self$bipartite_rsienaDV 11 2 0
## 200 Network 0 self$bipartite_rsienaDV 5 1 0
## 201 Network 0 self$bipartite_rsienaDV 2 7 0
## 202 Network 0 self$bipartite_rsienaDV 6 1 0
## 203 Network 0 self$bipartite_rsienaDV 7 8 0
## 204 Network 0 self$bipartite_rsienaDV 8 9 0
## 205 Network 0 self$bipartite_rsienaDV 2 4 0
## 206 Network 0 self$bipartite_rsienaDV 12 10 0
## 207 Network 0 self$bipartite_rsienaDV 1 8 0
## 208 Network 0 self$bipartite_rsienaDV 9 10 0
## 209 Network 0 self$bipartite_rsienaDV 6 1 0
## 210 Network 0 self$bipartite_rsienaDV 10 8 0
## 211 Network 0 self$bipartite_rsienaDV 6 6 0
## 212 Network 0 self$bipartite_rsienaDV 12 3 0
## 213 Network 0 self$bipartite_rsienaDV 9 2 0
## 214 Network 0 self$bipartite_rsienaDV 4 10 0
## 215 Network 0 self$bipartite_rsienaDV 2 1 0
## 216 Network 0 self$bipartite_rsienaDV 10 8 0
## 217 Network 0 self$bipartite_rsienaDV 9 7 0
## 218 Network 0 self$bipartite_rsienaDV 11 4 0
## 219 Network 0 self$bipartite_rsienaDV 8 4 0
## 220 Network 0 self$bipartite_rsienaDV 3 8 0
## 221 Network 0 self$bipartite_rsienaDV 6 5 0
## 222 Network 0 self$bipartite_rsienaDV 5 8 0
## 223 Network 0 self$bipartite_rsienaDV 8 2 0
## 224 Network 0 self$bipartite_rsienaDV 2 3 0
## 225 Network 0 self$bipartite_rsienaDV 10 5 0
## 226 Network 0 self$bipartite_rsienaDV 10 2 0
## 227 Network 0 self$bipartite_rsienaDV 2 7 0
## 228 Network 0 self$bipartite_rsienaDV 2 6 0
## 229 Network 0 self$bipartite_rsienaDV 3 1 0
## 230 Network 0 self$bipartite_rsienaDV 3 5 0
## 231 Network 0 self$bipartite_rsienaDV 9 9 0
## 232 Network 0 self$bipartite_rsienaDV 9 3 0
## 233 Network 0 self$bipartite_rsienaDV 7 3 0
## 234 Network 0 self$bipartite_rsienaDV 7 2 0
## 235 Network 0 self$bipartite_rsienaDV 12 10 0
## 236 Network 0 self$bipartite_rsienaDV 3 4 0
## 237 Network 0 self$bipartite_rsienaDV 8 3 0
## 238 Network 0 self$bipartite_rsienaDV 6 9 0
## 239 Network 0 self$bipartite_rsienaDV 8 2 0
## 240 Network 0 self$bipartite_rsienaDV 8 10 0
## 241 Network 0 self$bipartite_rsienaDV 3 10 0
## 242 Network 0 self$bipartite_rsienaDV 10 5 0
## 243 Network 0 self$bipartite_rsienaDV 1 10 0
## 244 Network 0 self$bipartite_rsienaDV 8 4 0
## 245 Network 0 self$bipartite_rsienaDV 5 6 0
## 246 Network 0 self$bipartite_rsienaDV 11 4 0
## 247 Network 0 self$bipartite_rsienaDV 2 2 0
## 248 Network 0 self$bipartite_rsienaDV 1 2 0
## 249 Network 0 self$bipartite_rsienaDV 4 2 0
## 250 Network 0 self$bipartite_rsienaDV 10 2 0
## 251 Network 0 self$bipartite_rsienaDV 1 4 0
## 252 Network 0 self$bipartite_rsienaDV 11 2 0
## 253 Network 0 self$bipartite_rsienaDV 9 5 0
## 254 Network 0 self$bipartite_rsienaDV 3 4 0
## 255 Network 0 self$bipartite_rsienaDV 5 1 0
## 256 Network 0 self$bipartite_rsienaDV 5 6 0
## 257 Network 0 self$bipartite_rsienaDV 4 2 0
## 258 Network 0 self$bipartite_rsienaDV 6 9 0
## 259 Network 0 self$bipartite_rsienaDV 8 2 0
## 260 Network 0 self$bipartite_rsienaDV 1 4 0
## 261 Network 0 self$bipartite_rsienaDV 8 5 0
## 262 Network 0 self$bipartite_rsienaDV 1 10 0
## 263 Network 0 self$bipartite_rsienaDV 7 8 0
## 264 Network 0 self$bipartite_rsienaDV 4 4 0
## 265 Network 0 self$bipartite_rsienaDV 3 4 0
## 266 Network 0 self$bipartite_rsienaDV 5 5 0
## 267 Network 0 self$bipartite_rsienaDV 6 7 0
## 268 Network 0 self$bipartite_rsienaDV 5 1 0
## 269 Network 0 self$bipartite_rsienaDV 8 10 0
## 270 Network 0 self$bipartite_rsienaDV 12 6 0
## 271 Network 0 self$bipartite_rsienaDV 7 5 0
## 272 Network 0 self$bipartite_rsienaDV 11 2 0
## 273 Network 0 self$bipartite_rsienaDV 5 4 0
## 274 Network 0 self$bipartite_rsienaDV 9 5 0
## 275 Network 0 self$bipartite_rsienaDV 8 3 0
## 276 Network 0 self$bipartite_rsienaDV 11 5 0
## 277 Network 0 self$bipartite_rsienaDV 1 9 0
## 278 Network 0 self$bipartite_rsienaDV 4 2 0
## 279 Network 0 self$bipartite_rsienaDV 12 10 0
## 280 Network 0 self$bipartite_rsienaDV 5 9 0
## 281 Network 0 self$bipartite_rsienaDV 1 8 0
## 282 Network 0 self$bipartite_rsienaDV 10 1 0
## 283 Network 0 self$bipartite_rsienaDV 2 3 0
## 284 Network 0 self$bipartite_rsienaDV 11 5 0
## 285 Network 0 self$bipartite_rsienaDV 8 6 0
## 286 Network 0 self$bipartite_rsienaDV 9 9 0
## 287 Network 0 self$bipartite_rsienaDV 6 1 0
## 288 Network 0 self$bipartite_rsienaDV 9 7 0
## 289 Network 0 self$bipartite_rsienaDV 10 8 0
## 290 Network 0 self$bipartite_rsienaDV 4 6 0
## 291 Network 0 self$bipartite_rsienaDV 8 10 0
## 292 Network 0 self$bipartite_rsienaDV 8 4 0
## 293 Network 0 self$bipartite_rsienaDV 5 6 0
## 294 Network 0 self$bipartite_rsienaDV 6 5 0
## 295 Network 0 self$bipartite_rsienaDV 1 4 0
## 296 Network 0 self$bipartite_rsienaDV 12 10 0
## 297 Network 0 self$bipartite_rsienaDV 1 4 0
## 298 Network 0 self$bipartite_rsienaDV 9 5 0
## 299 Network 0 self$bipartite_rsienaDV 10 5 0
## 300 Network 0 self$bipartite_rsienaDV 7 2 0
## reciprocal_rate LogOptionSetProb LogChoiceProb diagonal stability
## 1 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 2 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 3 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 4 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 5 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 6 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 7 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 8 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 9 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 10 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 11 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 12 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 13 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 14 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 15 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 16 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 17 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 18 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 19 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 20 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 21 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 22 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 23 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 24 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 25 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 26 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 27 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 28 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 29 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 30 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 31 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 32 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 33 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 34 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 35 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 36 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 37 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 38 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 39 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 40 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 41 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 42 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 43 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 44 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 45 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 46 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 47 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 48 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 49 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 50 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 51 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 52 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 53 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 54 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 55 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 56 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 57 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 58 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 59 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 60 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 61 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 62 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 63 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 64 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 65 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 66 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 67 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 68 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 69 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 70 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 71 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 72 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 73 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 74 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 75 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 76 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 77 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 78 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 79 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 80 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 81 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 82 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 83 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 84 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 85 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 86 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 87 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 88 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 89 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 90 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 91 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 92 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 93 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 94 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 95 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 96 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 97 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 98 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 99 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 100 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 101 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 102 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 103 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 104 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 105 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 106 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 107 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 108 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 109 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 110 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 111 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 112 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 113 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 114 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 115 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 116 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 117 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 118 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 119 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 120 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 121 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 122 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 123 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 124 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 125 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 126 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 127 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 128 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 129 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 130 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 131 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 132 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 133 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 134 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 135 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 136 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 137 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 138 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 139 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 140 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 141 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 142 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 143 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 144 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 145 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 146 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 147 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 148 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 149 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 150 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 151 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 152 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 153 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 154 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 155 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 156 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 157 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 158 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 159 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 160 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 161 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 162 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 163 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 164 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 165 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 166 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 167 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 168 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 169 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 170 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 171 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 172 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 173 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 174 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 175 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 176 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 177 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 178 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 179 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 180 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 181 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 182 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 183 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 184 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 185 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 186 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 187 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 188 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 189 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 190 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 191 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 192 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 193 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 194 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 195 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 196 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 197 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 198 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 199 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 200 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 201 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 202 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 203 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 204 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 205 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 206 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 207 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 208 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 209 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 210 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 211 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 212 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 213 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 214 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 215 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 216 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 217 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 218 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 219 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 220 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 221 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 222 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 223 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 224 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 225 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 226 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 227 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 228 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 229 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 230 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 231 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 232 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 233 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 234 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 235 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 236 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 237 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 238 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 239 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 240 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 241 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 242 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 243 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 244 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 245 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 246 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 247 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 248 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 249 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 250 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 251 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 252 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 253 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 254 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 255 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 256 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 257 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 258 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 259 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 260 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 261 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 262 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 263 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 264 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 265 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 266 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 267 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 268 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 269 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 270 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 271 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 272 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 273 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 274 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 275 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 276 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 277 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 278 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 279 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 280 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 281 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 282 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 283 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 284 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 285 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 286 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 287 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 288 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 289 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 290 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 291 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 292 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 293 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 294 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 295 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 296 0.08333333 -2.484907 -2.302585 FALSE TRUE
## 297 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 298 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 299 0.08333333 -2.484907 -2.302585 FALSE FALSE
## 300 0.08333333 -2.484907 -2.302585 FALSE FALSE
## tie_change chain_step_id
## 1 TRUE 1
## 2 TRUE 2
## 3 TRUE 3
## 4 TRUE 4
## 5 TRUE 5
## 6 FALSE 5
## 7 TRUE 7
## 8 TRUE 8
## 9 TRUE 9
## 10 TRUE 10
## 11 TRUE 11
## 12 TRUE 12
## 13 FALSE 12
## 14 TRUE 14
## 15 TRUE 15
## 16 TRUE 16
## 17 FALSE 16
## 18 TRUE 18
## 19 TRUE 19
## 20 TRUE 20
## 21 FALSE 20
## 22 TRUE 22
## 23 TRUE 23
## 24 TRUE 24
## 25 FALSE 24
## 26 TRUE 26
## 27 TRUE 27
## 28 TRUE 28
## 29 FALSE 28
## 30 FALSE 28
## 31 TRUE 31
## 32 TRUE 32
## 33 FALSE 32
## 34 TRUE 34
## 35 TRUE 35
## 36 TRUE 36
## 37 TRUE 37
## 38 TRUE 38
## 39 TRUE 39
## 40 TRUE 40
## 41 FALSE 40
## 42 TRUE 42
## 43 TRUE 43
## 44 TRUE 44
## 45 TRUE 45
## 46 FALSE 45
## 47 TRUE 47
## 48 TRUE 48
## 49 TRUE 49
## 50 TRUE 50
## 51 FALSE 50
## 52 FALSE 50
## 53 TRUE 53
## 54 TRUE 54
## 55 TRUE 55
## 56 TRUE 56
## 57 TRUE 57
## 58 TRUE 58
## 59 TRUE 59
## 60 TRUE 60
## 61 TRUE 61
## 62 TRUE 62
## 63 TRUE 63
## 64 TRUE 64
## 65 TRUE 65
## 66 TRUE 66
## 67 FALSE 66
## 68 FALSE 66
## 69 TRUE 69
## 70 TRUE 70
## 71 TRUE 71
## 72 FALSE 71
## 73 TRUE 73
## 74 TRUE 74
## 75 TRUE 75
## 76 TRUE 76
## 77 TRUE 77
## 78 TRUE 78
## 79 TRUE 79
## 80 TRUE 80
## 81 TRUE 81
## 82 TRUE 82
## 83 TRUE 83
## 84 TRUE 84
## 85 TRUE 85
## 86 TRUE 86
## 87 TRUE 87
## 88 TRUE 88
## 89 FALSE 88
## 90 TRUE 90
## 91 TRUE 91
## 92 TRUE 92
## 93 TRUE 93
## 94 TRUE 94
## 95 TRUE 95
## 96 TRUE 96
## 97 TRUE 97
## 98 TRUE 98
## 99 TRUE 99
## 100 TRUE 100
## 101 FALSE 100
## 102 TRUE 102
## 103 TRUE 103
## 104 TRUE 104
## 105 TRUE 105
## 106 TRUE 106
## 107 TRUE 107
## 108 TRUE 108
## 109 TRUE 109
## 110 TRUE 110
## 111 TRUE 111
## 112 TRUE 112
## 113 TRUE 113
## 114 TRUE 114
## 115 TRUE 115
## 116 TRUE 116
## 117 TRUE 117
## 118 TRUE 118
## 119 TRUE 119
## 120 TRUE 120
## 121 TRUE 121
## 122 TRUE 122
## 123 TRUE 123
## 124 FALSE 123
## 125 TRUE 125
## 126 TRUE 126
## 127 TRUE 127
## 128 TRUE 128
## 129 TRUE 129
## 130 TRUE 130
## 131 TRUE 131
## 132 TRUE 132
## 133 TRUE 133
## 134 TRUE 134
## 135 TRUE 135
## 136 TRUE 136
## 137 TRUE 137
## 138 FALSE 137
## 139 TRUE 139
## 140 TRUE 140
## 141 TRUE 141
## 142 TRUE 142
## 143 TRUE 143
## 144 TRUE 144
## 145 FALSE 144
## 146 TRUE 146
## 147 TRUE 147
## 148 FALSE 147
## 149 TRUE 149
## 150 TRUE 150
## 151 TRUE 151
## 152 TRUE 152
## 153 TRUE 153
## 154 TRUE 154
## 155 TRUE 155
## 156 TRUE 156
## 157 FALSE 156
## 158 TRUE 158
## 159 TRUE 159
## 160 TRUE 160
## 161 TRUE 161
## 162 TRUE 162
## 163 TRUE 163
## 164 TRUE 164
## 165 TRUE 165
## 166 FALSE 165
## 167 TRUE 167
## 168 TRUE 168
## 169 TRUE 169
## 170 TRUE 170
## 171 TRUE 171
## 172 TRUE 172
## 173 TRUE 173
## 174 TRUE 174
## 175 TRUE 175
## 176 FALSE 175
## 177 TRUE 177
## 178 TRUE 178
## 179 FALSE 178
## 180 TRUE 180
## 181 FALSE 180
## 182 FALSE 180
## 183 FALSE 180
## 184 TRUE 184
## 185 TRUE 185
## 186 TRUE 186
## 187 TRUE 187
## 188 TRUE 188
## 189 TRUE 189
## 190 TRUE 190
## 191 TRUE 191
## 192 TRUE 192
## 193 TRUE 193
## 194 TRUE 194
## 195 TRUE 195
## 196 TRUE 196
## 197 TRUE 197
## 198 TRUE 198
## 199 TRUE 199
## 200 TRUE 200
## 201 TRUE 201
## 202 TRUE 202
## 203 TRUE 203
## 204 TRUE 204
## 205 TRUE 205
## 206 FALSE 205
## 207 TRUE 207
## 208 FALSE 207
## 209 TRUE 209
## 210 TRUE 210
## 211 TRUE 211
## 212 TRUE 212
## 213 TRUE 213
## 214 FALSE 213
## 215 TRUE 215
## 216 TRUE 216
## 217 TRUE 217
## 218 TRUE 218
## 219 TRUE 219
## 220 TRUE 220
## 221 TRUE 221
## 222 TRUE 222
## 223 TRUE 223
## 224 TRUE 224
## 225 TRUE 225
## 226 TRUE 226
## 227 TRUE 227
## 228 TRUE 228
## 229 TRUE 229
## 230 TRUE 230
## 231 TRUE 231
## 232 TRUE 232
## 233 TRUE 233
## 234 TRUE 234
## 235 FALSE 234
## 236 TRUE 236
## 237 TRUE 237
## 238 TRUE 238
## 239 TRUE 239
## 240 FALSE 239
## 241 FALSE 239
## 242 TRUE 242
## 243 FALSE 242
## 244 TRUE 244
## 245 TRUE 245
## 246 TRUE 246
## 247 TRUE 247
## 248 TRUE 248
## 249 TRUE 249
## 250 TRUE 250
## 251 TRUE 251
## 252 TRUE 252
## 253 TRUE 253
## 254 TRUE 254
## 255 TRUE 255
## 256 TRUE 256
## 257 TRUE 257
## 258 TRUE 258
## 259 TRUE 259
## 260 TRUE 260
## 261 TRUE 261
## 262 FALSE 261
## 263 TRUE 263
## 264 TRUE 264
## 265 TRUE 265
## 266 TRUE 266
## 267 TRUE 267
## 268 TRUE 268
## 269 FALSE 268
## 270 TRUE 270
## 271 TRUE 271
## 272 TRUE 272
## 273 TRUE 273
## 274 TRUE 274
## 275 TRUE 275
## 276 TRUE 276
## 277 TRUE 277
## 278 TRUE 278
## 279 FALSE 278
## 280 TRUE 280
## 281 TRUE 281
## 282 TRUE 282
## 283 TRUE 283
## 284 TRUE 284
## 285 TRUE 285
## 286 TRUE 286
## 287 TRUE 287
## 288 TRUE 288
## 289 TRUE 289
## 290 TRUE 290
## 291 FALSE 290
## 292 TRUE 292
## 293 TRUE 293
## 294 TRUE 294
## 295 TRUE 295
## 296 FALSE 295
## 297 TRUE 297
## 298 TRUE 298
## 299 TRUE 299
## 300 TRUE 300
##
## 33.33%
## 66.67%
## 100.00%
## ===================================================================================================
## Model 1
## ---------------------------------------------------------------------------------------------------
## Rate parameter period 1 0.0792 (0.0787)
## outdegree (density) 0.0000 (0.0000)
## indegree - popularity 0.0000 (0.0000)
## outdegree - activity 0.0000 (0.0000)
## XW=>X closure of self$component_1_coDyadCovar 1.0000 (0.0000) ***
## self$component_2_coDyadCovar 0.0000 (0.0000)
## self$strat_1_coCovar ego 0.0000 (0.0000)
## ind. pop.^(1/0) weighted self$strat_2_coCovar 0.0000 (0.0000)
## self$strat_1_coCovar ego x XW=>X closure of self$component_1_coDyadCovar 0.0000 (0.0000)
## ind. pop.^(1/0) weighted self$strat_2_coCovar x self$component_2_coDyadCovar 0.0000 (0.0000)
## ---------------------------------------------------------------------------------------------------
## Iterations 300
## ===================================================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05## 1st and last state of the bipartite matrix system
print(env1$bipartite_matrix_init )## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 0 0 0 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0 0 0 0
## [6,] 0 0 0 0 0 0 0 0 0
## [7,] 0 0 0 0 0 0 0 0 0
## [8,] 0 0 0 0 0 0 0 0 0
## [9,] 0 0 0 0 0 0 0 0 0
## [10,] 0 0 0 0 0 0 0 0 0
## [11,] 0 0 0 0 0 0 0 0 0
## [12,] 0 0 0 0 0 0 0 0 0print(env1$bi_env_arr[,, dim(env1$bi_env_arr)[3] ] )## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 1 0 0 1 1 0 1 0
## [2,] 1 0 1 0 0 1 0 0 1
## [3,] 1 1 0 0 0 0 0 0 0
## [4,] 0 0 1 0 0 1 0 0 0
## [5,] 0 0 1 0 0 0 0 0 0
## [6,] 1 1 1 0 0 1 0 0 0
## [7,] 0 1 1 0 1 0 0 1 1
## [8,] 1 0 1 1 0 1 1 0 1
## [9,] 1 1 1 1 0 1 1 1 0
## [10,] 0 1 1 1 1 1 0 1 0
## [11,] 0 0 1 1 0 0 1 1 0
## [12,] 0 1 0 0 1 0 1 1 01.4 Print Bipartite Search Network Snapshots
Snapshopts of the the biparite network and the social and component epistasis interactions are taken at fixed step intervals to show the evolving multidimensional coupled search environment (actors and components)
# snapshot_ids <- round( seq(1, dim(env1$bi_env_arr)[3], length=3 ) )
# snapshot_ids <- c(1, 1+ (1:6)*env1$M)
snapshot_ids <- c(1, (1:8)*10, dim(env1$bi_env_arr)[3] )
for (i in 1:length(snapshot_ids)) {
step <- snapshot_ids[ i ]
mat <- env1$bi_env_arr[,,step]
env1$plot_bipartite_system_from_mat(mat, step)
}
1.5 Plot Actor Degrees (Component Scope and Common Affiliation Social Ties)
Time series is simulated decision steps.
##
env1$plot_actor_degrees(loess_span = 0.35)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.6 Plot Component Degrees (Membership/Popularity and Common Actor Epistasis Ties)
##
env1$plot_component_degrees(loess_span = 0.35)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.7 Plot Utility by Strategy over Time (Simulated Decision Steps)
## [1] 3600 5## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.8 Plot Utility by Actor over Time (Simulated Decision Steps)
## [1] 3600 5## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.9 Plot Contributions to Actor Utility colored by Strategy
Time series is simulated decision steps.
##
env1$plot_utility_components(loess_span=0.25)## `geom_smooth()` using formula = 'y ~ x'